We start with n= 75. In these analyses, I used a stringent pipeline, censoring and interpolating over vols with > FD of 0.5 mm or DVARS > 1.75. Those are excluded from the timeseries used to calculate the networks used here. I excluded anyone with > 50% frames censored (following Yeo, 2011) or mean motion > 1 mm, or max motion over 10 mm, leaving us with 70 participants, 26 of which have a second run.
The dataframe below shows all runs for subjects who meet this criteria. At the moment, I’m just examining the first run, not averaging in those who have a second run.
| No | Variable | Stats / Values | Freqs (% of Valid) | Graph | Valid | Missing |
|---|---|---|---|---|---|---|
| 1 | age_scan [numeric] | mean (sd) : 6.98 (1.31) min < med < max : 4.11 < 7 < 10.59 IQR (CV) : 1.95 (0.19) | 68 distinct values | 92 (100%) | 0 (0%) | |
| 2 | run [factor] | 1. run-01 2. run-02 3. run-03 | 65 (70.7%) 26 (28.3%) 1 (1.1%) | 92 (100%) | 0 (0%) | |
| 3 | pctSpikesFD [numeric] | mean (sd) : 0.09 (0.1) min < med < max : 0 < 0.06 < 0.36 IQR (CV) : 0.11 (1.04) | 69 distinct values | 92 (100%) | 0 (0%) | |
| 4 | relMeanRMSMotion [numeric] | mean (sd) : 0.27 (0.16) min < med < max : 0.04 < 0.22 < 0.78 IQR (CV) : 0.18 (0.6) | 92 distinct values | 92 (100%) | 0 (0%) | |
| 5 | nSpikesDV [integer] | mean (sd) : 11.03 (9.16) min < med < max : 0 < 9 < 35 IQR (CV) : 15 (0.83) | 30 distinct values | 92 (100%) | 0 (0%) | |
| 6 | relMaxRMSMotion [numeric] | mean (sd) : 2.68 (2.3) min < med < max : 0.13 < 2 < 9.64 IQR (CV) : 2.99 (0.86) | 92 distinct values | 92 (100%) | 0 (0%) |
Generated by summarytools 0.8.7 (R version 3.4.3)
2019-08-11
| No | Variable | Stats / Values | Freqs (% of Valid) | Graph | Valid | Missing |
|---|---|---|---|---|---|---|
| 1 | race2 [factor] | 1. White 2. Black 3. Other | 17 (25.4%) 41 (61.2%) 9 (13.4%) | 67 (95.71%) | 3 (4.29%) | |
| 2 | ethnicity [factor] | 1. Not Hispanic or Latino 2. Hispanic or Latino | 60 (85.7%) 10 (14.3%) | 70 (100%) | 0 (0%) | |
| 3 | has_diagnoses [integer] | mean (sd) : 0.03 (0.17) min < med < max : 0 < 0 < 1 IQR (CV) : 0 (5.87) | 0 : 68 (97.1%) 1 : 2 (2.9%) | 70 (100%) | 0 (0%) | |
| 4 | parent1_edu [integer] | mean (sd) : 14.9 (2.71) min < med < max : 12 < 14 < 20 IQR (CV) : 6 (0.18) | 12 : 26 (37.7%) 14 : 10 (14.5%) 16 : 13 (18.8%) 18 : 16 (23.2%) 20 : 4 (5.8%) | 69 (98.57%) | 1 (1.43%) | |
| 5 | parent2_edu [integer] | mean (sd) : 13.46 (4.36) min < med < max : 0 < 12 < 20 IQR (CV) : 4 (0.32) | 0 : 3 (5.1%) 10 : 4 (6.8%) 12 : 27 (45.8%) 14 : 4 (6.8%) 16 : 8 (13.6%) 18 : 7 (11.9%) 20 : 6 (10.2%) | 59 (84.29%) | 11 (15.71%) | |
| 6 | income_median [integer] | mean (sd) : 62761.19 (55231.08) min < med < max : 2500 < 42500 < 2e+05 IQR (CV) : 67000 (0.88) | 11 distinct values | 67 (95.71%) | 3 (4.29%) | |
| 7 | monthslive_iflostincome [integer] | mean (sd) : 2.7 (1.47) min < med < max : 1 < 3 < 5 IQR (CV) : 3 (0.54) | 1 : 20 (30.3%) 2 : 11 (16.7%) 3 : 16 (24.2%) 4 : 7 (10.6%) 5 : 12 (18.2%) | 66 (94.29%) | 4 (5.71%) | |
| 8 | childaces_sum_ignorenan [integer] | mean (sd) : 1.11 (1.37) min < med < max : 0 < 1 < 6 IQR (CV) : 2 (1.23) | 0 : 32 (45.7%) 1 : 16 (22.9%) 2 : 11 (15.7%) 3 : 7 (10.0%) 4 : 2 (2.9%) 5 : 1 (1.4%) 6 : 1 (1.4%) | 70 (100%) | 0 (0%) |
Generated by summarytools 0.8.7 (R version 3.4.3)
2019-08-11
WPPSI and WISC scores are not totally finalized, so decided not to look at cognition for the moment, unless environmental effects are not interesting. For the future.
We find that overall, all measures of network segregation significantly increase with age in our dataset, with remarkable consistency. We examined within-network connectivity, between network connectivity, system segregation (as calculated in Chan et al. 2018), the modularity quality index, and the participation coefficient (summed across negative and positive weights).
We controlled for sex, mean framewise displacement, percent of FD spikes in the data, the number of volumes a participant had, and the average weight of the functional network.
The number of communities detected using modularity maximization on this data does not significantly decrease with age (each subject ran 100x, averaged Q and k, range in k is 2.5, 4.77). ###Non-linear effects of age?
We also used restricted least ratio tests to test for the presence of non-linearity in our data. As you can see in the plots below, which use GAMs, most measures are linear and look no different from the linear models above.
Tests of non-linearity confirmed that no non-linear effects are present. The wiggly participation coefficient line is just over-fitting.
We fit the same model, controlling for sex, mean framewise displacement, percent of FD spikes in the data, the number of volumes a participant had, and the average weight of the functional network, to between- and within-system connectivity for each of the Yeo 7 systems.
We find the strongest effects are in the default mode system, between default and attentional networks.
None of these showed significantly non-linear effects, either.
Effects on default mode connectivity survive FDR correction across the tests conducted, with DMN-VAN p_fdr=0.0078839 and DMN-DAN p_fdr= 0.0094245. Vis-DAN p_fdr= 0.0756036.
In summary, there are no main effects of SES, race, or ACES sum on any of the measures of network segregation.
Using the same models as above, age_scan + male + fd_mean + avgweight + pctSpikesFD + size_t, we see no significant associations between any of the measures of network segregation and either parent 1 education, family income (median of bin), or an SES composite of the two. Race is also not significantly associated with any of the measures of network segregation. I also looked at the sum of child ACES, which doesn’t show any significant associations in the model above.
However, there are effects of the binned ACES (into 0-1, 2, or 3+) on within- and between-network connectivity as well as the participation coefficient. We see that 2 ACES is associated with significantly less within- and more between-network connectivity (p=0.0243742), and a higher average participation coefficient (p=0.0419415), lower system segregation (p=0.0486666).
No significant interactions between any of the environmental variables above and age. However, age x income is marginally predictive of within- and between-network connectivity.
p=0.0884959 for within-network connectivity and p=0.0884959 for between-network connectivity. However, this does not seem consistent across different SES metrics (education vs. income, etc.)
I decided to look specifically at the networks that show strong age effects in our age range, that is, visual to dorsal attention and DMN to attentional networks, with the idea that previously the systems that showed stronger age effects also showed environmental interactions.
Visual to DAN
DMN to DAN
DMN to VAN
As Allyson suggested on 8/6, I ran models looking for a main effect of environment across all networks and fdr-corrected. I chose the SES composite for this analysis, since that seemed the most principled. No networks showed significant main effects of the environment after FDR correction. Networks that showed marginal effects of the environment were MOT-FPN, p_FDR=0.0777552, VAN-LIM p_fdr=0.0740308, and LIM-LIM *p_fdr= .
Three-category ACES also does not show a significant environmental main effect after FDR-correction.
Visual to DAN
DMN to DAN
DMN to VAN
As suggested by Allyson on 8/6, I also ran some models looking at whether VIS-VIS, DMN-DMN, or FPN-DMN show age x SES interactions.
Vis to Vis
DMN to DMN
DMN to FPN
To examine whether the age x SES interactions in visual areas are accounted for by stress or cognitive enrichment, I first sought to characterize these two.
Per Allyson on 8/9, used ACES sum score, PSS sum score, and WLBQ individual items for stress, along with the first two items on the Neighborhood questionnaire. A factor analysis of stress variables pulled from the PSS, ACES sum, WLBQ, and the first two items on the neighborhood questionnaire revealed that we should extract 2 (or 4) factors, depending on which scree plot you look at.
I extracted the second factor in the factor analysis, which loaded more heavily on child ACES and PSS sum than the first factor, which was dominated by the WLBQ questions. Regardless of which factor I take, though, or which package I use, the results do not change.
I examined whether it accounted for the age x SES composite effect on visual networks. It does not. However, note that there is slightly less power to detect an interaction with 59 as opposed to 65 subjects who have all data for the stress factor, but when examining only that subset, the age x SES composite on visual networks still holds, even when controlling for the first factor of stress.
I’m not completely sure about the correctness of the approach I took, but did try two different factor analysis packages, extracted the second or first factor (which was slightly different) using both, and examined it here, and get similar results.
Also, this factor does not significantly predict any main effects on any specific networks after FDR correction across networks, or any whole-brain segregation measures. It is negatively correlated with SES (!), but positively correlated with PSS and child ACES (obviously). Wait and see whether cognitive enrichment is predictive.
I used the HOME subscale scores, as well as the literacy and numeracy questionnaire question about the number of child books and the number of hours per week the child was read to. I also included literacy and numeracy subscales for frequency of other activities at home: Music, Visual Arts, Pretend Play, Spatial, Fine Motor.
Examining a scree plot yields that either 3 or 2 factors should be extracted. N=59 have information on cognitive stimulation.
We see that when including the first factor of cognitive stimulation, which loads mostly on the literacy and numeracy questionnaire, and less on the HOME or the number of child books, it partially accounts for the effect of SES on visual- DAN connectivity. The age x cognitive stimulation interaction is marginally significant, and including the first factor in the age* ses_composite model reduces the interaction to marginal.
It is negatively correlated with SES, strongly so (!), and slightly positively correlated with child ACES! Odd. Looks like in our sample, HOME is weakly positively correlated with SES, but Litnum is even more stronly negatively correlated with SES.
Also, this factor does not significantly predict any main effects on any specific networks after FDR correction across networks, or any whole-brain segregation measures or interactions with age.
There are also sometimes outliers on some of the measures that flatten out age or environment trends, I have been looking into using robust regression to examine these trends instead, but did not go through all the trouble to bootstrap p-values for this time around. However, I think it’s possible that robust regression might be a more defensible choice.
Can you do this for networks somehow–is this relevant? Graham has done it before, maybe?
We used Schaefer200 as the replication parcellation, since it has nice correspondence to Schaefer400 and the Yeo7 systems.
All previous findings significant increases in network segregation with age hold in our replication parcellation! The only finding that is inconsistent is the number of communities detected using modularity maximization decreasing with age, which was likely spurious anyways.
As a reminder, we controlled for sex, mean framewise displacement, percent of FD spikes in the data, the number of volumes a participant had, and the average weight of the functional network.
Also, no non-linear effects of age.
However, as above, there are effects of the binned ACES (into 0-1, 2, or 3+) on within- and between-network connectivity as well as the participation coefficient. We see that 2 ACES is associated with significantly less within- and more between-network connectivity (p=0.0243742), and a higher average participation coefficient (p=0.0419415), lower system segregation (p=0.0486666).
Again, we find the strongest effects in Vis-DAN, DMN-DAN, and DMN-VAN, which are the only effects that pass fdr correction.
All three effects survive FDR correction across the tests conducted, with DMN-VAN p_fdr=, DMN-DAN p_fdr= , Vis-DAN p_fdr= .
Again, the effects shown above replicate.
Visual to DAN
DMN to DAN
DMN to DAN
As Allyson suggested on 8/6, I ran models instead looking for a main effect of environment across all networks and fdr-corrected in the replication parcellation as well. I chose the SES composite for this analysis, since that seemed the most principled. No networks showed significant or marginal main effects of the environment after FDR correction.
Visual to DAN
DMN to DAN
DMN to VAN
Vis to Vis
DMN to DMN
DMN to FPN